Overview

Dataset statistics

Number of variables17
Number of observations166800
Missing cells395
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.6 MiB
Average record size in memory136.0 B

Variable types

Text6
Categorical4
Numeric7

Alerts

2020 Census Tract is highly overall correlated with StateHigh correlation
Clean Alternative Fuel Vehicle (CAFV) Eligibility is highly overall correlated with Electric Range and 2 other fieldsHigh correlation
Electric Range is highly overall correlated with Clean Alternative Fuel Vehicle (CAFV) Eligibility and 2 other fieldsHigh correlation
Electric Vehicle Type is highly overall correlated with Clean Alternative Fuel Vehicle (CAFV) Eligibility and 2 other fieldsHigh correlation
Legislative District is highly overall correlated with StateHigh correlation
Make is highly overall correlated with Clean Alternative Fuel Vehicle (CAFV) Eligibility and 1 other fieldsHigh correlation
Model Year is highly overall correlated with Electric RangeHigh correlation
Postal Code is highly overall correlated with StateHigh correlation
State is highly overall correlated with 2020 Census Tract and 2 other fieldsHigh correlation
State is highly imbalanced (99.4%)Imbalance
Postal Code is highly skewed (γ1 = -30.09608692)Skewed
2020 Census Tract is highly skewed (γ1 = -26.96677648)Skewed
DOL Vehicle ID has unique valuesUnique
Electric Range has 83517 (50.1%) zerosZeros
Base MSRP has 163437 (98.0%) zerosZeros

Reproduction

Analysis started2024-02-04 09:22:58.579780
Analysis finished2024-02-04 09:23:40.608845
Duration42.03 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Distinct10316
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2024-02-04T09:23:40.959439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1668000
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1969 ?
Unique (%)1.2%

Sample

1st row3C3CFFGE4E
2nd row5YJXCBE40H
3rd row3MW39FS03P
4th row7PDSGABA8P
5th row5YJ3E1EB8L
ValueCountFrequency (%)
7saygdee6p 1114
 
0.7%
7saygdee7p 1090
 
0.7%
7saygdeexp 1071
 
0.6%
7saygdee8p 1071
 
0.6%
7saygdee0p 1052
 
0.6%
7saygdee9p 1041
 
0.6%
7saygdee5p 1037
 
0.6%
7saygdee2p 1029
 
0.6%
7saygdee1p 1022
 
0.6%
7saygdee3p 1014
 
0.6%
Other values (10306) 156259
93.7%
2024-02-04T09:23:41.671850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 150671
 
9.0%
1 121417
 
7.3%
A 104815
 
6.3%
Y 94860
 
5.7%
J 84900
 
5.1%
P 82186
 
4.9%
5 79952
 
4.8%
3 69313
 
4.2%
D 64150
 
3.8%
G 64092
 
3.8%
Other values (24) 751644
45.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1144298
68.6%
Decimal Number 523702
31.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 150671
13.2%
A 104815
 
9.2%
Y 94860
 
8.3%
J 84900
 
7.4%
P 82186
 
7.2%
D 64150
 
5.6%
G 64092
 
5.6%
C 56828
 
5.0%
N 55104
 
4.8%
S 53497
 
4.7%
Other values (14) 333195
29.1%
Decimal Number
ValueCountFrequency (%)
1 121417
23.2%
5 79952
15.3%
3 69313
13.2%
7 50634
9.7%
4 47016
 
9.0%
0 43598
 
8.3%
6 38554
 
7.4%
2 34411
 
6.6%
8 22088
 
4.2%
9 16719
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 1144298
68.6%
Common 523702
31.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 150671
13.2%
A 104815
 
9.2%
Y 94860
 
8.3%
J 84900
 
7.4%
P 82186
 
7.2%
D 64150
 
5.6%
G 64092
 
5.6%
C 56828
 
5.0%
N 55104
 
4.8%
S 53497
 
4.7%
Other values (14) 333195
29.1%
Common
ValueCountFrequency (%)
1 121417
23.2%
5 79952
15.3%
3 69313
13.2%
7 50634
9.7%
4 47016
 
9.0%
0 43598
 
8.3%
6 38554
 
7.4%
2 34411
 
6.6%
8 22088
 
4.2%
9 16719
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1668000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 150671
 
9.0%
1 121417
 
7.3%
A 104815
 
6.3%
Y 94860
 
5.7%
J 84900
 
5.1%
P 82186
 
4.9%
5 79952
 
4.8%
3 69313
 
4.2%
D 64150
 
3.8%
G 64092
 
3.8%
Other values (24) 751644
45.1%

County
Text

Distinct187
Distinct (%)0.1%
Missing5
Missing (%)< 0.1%
Memory size1.3 MiB
2024-02-04T09:23:42.023870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length20
Median length4
Mean length5.4757697
Min length3

Characters and Unicode

Total characters913331
Distinct characters52
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)0.1%

Sample

1st rowYakima
2nd rowThurston
3rd rowKing
4th rowSnohomish
5th rowKing
ValueCountFrequency (%)
king 86594
51.3%
snohomish 19570
 
11.6%
pierce 12972
 
7.7%
clark 9847
 
5.8%
thurston 6042
 
3.6%
kitsap 5522
 
3.3%
spokane 4312
 
2.6%
whatcom 4039
 
2.4%
benton 2028
 
1.2%
skagit 1842
 
1.1%
Other values (192) 16096
 
9.5%
2024-02-04T09:23:43.005106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 132573
14.5%
n 130137
14.2%
K 92994
10.2%
g 89098
9.8%
o 59669
 
6.5%
h 50618
 
5.5%
a 42008
 
4.6%
s 37326
 
4.1%
e 36776
 
4.0%
r 32990
 
3.6%
Other values (42) 209142
22.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 742390
81.3%
Uppercase Letter 168864
 
18.5%
Space Separator 2069
 
0.2%
Other Punctuation 7
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 132573
17.9%
n 130137
17.5%
g 89098
12.0%
o 59669
8.0%
h 50618
 
6.8%
a 42008
 
5.7%
s 37326
 
5.0%
e 36776
 
5.0%
r 32990
 
4.4%
m 26222
 
3.5%
Other values (15) 104973
14.1%
Uppercase Letter
ValueCountFrequency (%)
K 92994
55.1%
S 27086
 
16.0%
P 13255
 
7.8%
C 12763
 
7.6%
T 6047
 
3.6%
W 5238
 
3.1%
B 2051
 
1.2%
J 1863
 
1.1%
I 1797
 
1.1%
G 1139
 
0.7%
Other values (13) 4631
 
2.7%
Other Punctuation
ValueCountFrequency (%)
' 5
71.4%
. 2
 
28.6%
Space Separator
ValueCountFrequency (%)
2069
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 911254
99.8%
Common 2077
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 132573
14.5%
n 130137
14.3%
K 92994
10.2%
g 89098
9.8%
o 59669
 
6.5%
h 50618
 
5.6%
a 42008
 
4.6%
s 37326
 
4.1%
e 36776
 
4.0%
r 32990
 
3.6%
Other values (38) 207065
22.7%
Common
ValueCountFrequency (%)
2069
99.6%
' 5
 
0.2%
. 2
 
0.1%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 913331
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 132573
14.5%
n 130137
14.2%
K 92994
10.2%
g 89098
9.8%
o 59669
 
6.5%
h 50618
 
5.5%
a 42008
 
4.6%
s 37326
 
4.1%
e 36776
 
4.0%
r 32990
 
3.6%
Other values (42) 209142
22.9%

City
Text

Distinct704
Distinct (%)0.4%
Missing5
Missing (%)< 0.1%
Memory size1.3 MiB
2024-02-04T09:23:43.500911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length24
Median length23
Mean length8.215648
Min length3

Characters and Unicode

Total characters1370329
Distinct characters53
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique227 ?
Unique (%)0.1%

Sample

1st rowYakima
2nd rowOlympia
3rd rowRenton
4th rowBothell
5th rowKent
ValueCountFrequency (%)
seattle 27831
 
14.4%
bellevue 8364
 
4.3%
redmond 6032
 
3.1%
vancouver 5869
 
3.0%
bothell 5440
 
2.8%
kirkland 5028
 
2.6%
sammamish 4876
 
2.5%
island 4773
 
2.5%
renton 4617
 
2.4%
olympia 4058
 
2.1%
Other values (744) 116642
60.3%
2024-02-04T09:23:44.377649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 186942
13.6%
a 133494
 
9.7%
l 121337
 
8.9%
t 95467
 
7.0%
n 90732
 
6.6%
o 80841
 
5.9%
r 56904
 
4.2%
i 54412
 
4.0%
S 47328
 
3.5%
d 45930
 
3.4%
Other values (43) 456942
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1149695
83.9%
Uppercase Letter 193715
 
14.1%
Space Separator 26735
 
2.0%
Dash Punctuation 184
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 186942
16.3%
a 133494
11.6%
l 121337
10.6%
t 95467
 
8.3%
n 90732
 
7.9%
o 80841
 
7.0%
r 56904
 
4.9%
i 54412
 
4.7%
d 45930
 
4.0%
m 44789
 
3.9%
Other values (16) 238847
20.8%
Uppercase Letter
ValueCountFrequency (%)
S 47328
24.4%
B 24749
12.8%
R 13350
 
6.9%
L 10044
 
5.2%
K 10012
 
5.2%
M 9691
 
5.0%
V 9189
 
4.7%
T 8027
 
4.1%
I 7470
 
3.9%
P 7443
 
3.8%
Other values (15) 46412
24.0%
Space Separator
ValueCountFrequency (%)
26735
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 184
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1343410
98.0%
Common 26919
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 186942
13.9%
a 133494
 
9.9%
l 121337
 
9.0%
t 95467
 
7.1%
n 90732
 
6.8%
o 80841
 
6.0%
r 56904
 
4.2%
i 54412
 
4.1%
S 47328
 
3.5%
d 45930
 
3.4%
Other values (41) 430023
32.0%
Common
ValueCountFrequency (%)
26735
99.3%
- 184
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1370329
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 186942
13.6%
a 133494
 
9.7%
l 121337
 
8.9%
t 95467
 
7.0%
n 90732
 
6.6%
o 80841
 
5.9%
r 56904
 
4.2%
i 54412
 
4.0%
S 47328
 
3.5%
d 45930
 
3.4%
Other values (43) 456942
33.3%

State
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct44
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
WA
166440 
CA
 
91
VA
 
38
MD
 
32
TX
 
24
Other values (39)
 
175

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters333600
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowWA
2nd rowWA
3rd rowWA
4th rowWA
5th rowWA

Common Values

ValueCountFrequency (%)
WA 166440
99.8%
CA 91
 
0.1%
VA 38
 
< 0.1%
MD 32
 
< 0.1%
TX 24
 
< 0.1%
NC 14
 
< 0.1%
IL 13
 
< 0.1%
CO 12
 
< 0.1%
FL 10
 
< 0.1%
HI 9
 
< 0.1%
Other values (34) 117
 
0.1%

Length

2024-02-04T09:23:44.704216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wa 166440
99.8%
ca 91
 
0.1%
va 38
 
< 0.1%
md 32
 
< 0.1%
tx 24
 
< 0.1%
nc 14
 
< 0.1%
il 13
 
< 0.1%
co 12
 
< 0.1%
fl 10
 
< 0.1%
hi 9
 
< 0.1%
Other values (34) 117
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 166608
49.9%
W 166441
49.9%
C 138
 
< 0.1%
V 45
 
< 0.1%
M 43
 
< 0.1%
N 42
 
< 0.1%
D 39
 
< 0.1%
T 34
 
< 0.1%
L 33
 
< 0.1%
I 30
 
< 0.1%
Other values (15) 147
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 333600
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 166608
49.9%
W 166441
49.9%
C 138
 
< 0.1%
V 45
 
< 0.1%
M 43
 
< 0.1%
N 42
 
< 0.1%
D 39
 
< 0.1%
T 34
 
< 0.1%
L 33
 
< 0.1%
I 30
 
< 0.1%
Other values (15) 147
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 333600
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 166608
49.9%
W 166441
49.9%
C 138
 
< 0.1%
V 45
 
< 0.1%
M 43
 
< 0.1%
N 42
 
< 0.1%
D 39
 
< 0.1%
T 34
 
< 0.1%
L 33
 
< 0.1%
I 30
 
< 0.1%
Other values (15) 147
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 333600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 166608
49.9%
W 166441
49.9%
C 138
 
< 0.1%
V 45
 
< 0.1%
M 43
 
< 0.1%
N 42
 
< 0.1%
D 39
 
< 0.1%
T 34
 
< 0.1%
L 33
 
< 0.1%
I 30
 
< 0.1%
Other values (15) 147
 
< 0.1%

Postal Code
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct836
Distinct (%)0.5%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean98173.714
Minimum1730
Maximum99577
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-04T09:23:44.967491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile98006
Q198052
median98122
Q398371
95-th percentile98942
Maximum99577
Range97847
Interquartile range (IQR)319

Descriptive statistics

Standard deviation2442.5844
Coefficient of variation (CV)0.024880228
Kurtosis954.3603
Mean98173.714
Median Absolute Deviation (MAD)101
Skewness-30.096087
Sum1.6374885 × 1010
Variance5966218.6
MonotonicityNot monotonic
2024-02-04T09:23:45.265334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98052 4252
 
2.5%
98012 3115
 
1.9%
98033 2840
 
1.7%
98006 2663
 
1.6%
98004 2652
 
1.6%
98115 2553
 
1.5%
98074 2341
 
1.4%
98072 2312
 
1.4%
98188 2286
 
1.4%
98034 2237
 
1.3%
Other values (826) 139544
83.7%
ValueCountFrequency (%)
1730 1
< 0.1%
1731 1
< 0.1%
1824 1
< 0.1%
1908 1
< 0.1%
2842 1
< 0.1%
3804 1
< 0.1%
6355 1
< 0.1%
6371 1
< 0.1%
6379 2
< 0.1%
6385 1
< 0.1%
ValueCountFrequency (%)
99577 1
 
< 0.1%
99403 57
 
< 0.1%
99402 9
 
< 0.1%
99371 1
 
< 0.1%
99362 329
0.2%
99361 12
 
< 0.1%
99360 7
 
< 0.1%
99357 19
 
< 0.1%
99356 1
 
< 0.1%
99354 283
0.2%

Model Year
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.3418
Minimum1997
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-04T09:23:45.529747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile2014
Q12018
median2021
Q32023
95-th percentile2023
Maximum2024
Range27
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0014645
Coefficient of variation (CV)0.0014856222
Kurtosis0.45101207
Mean2020.3418
Median Absolute Deviation (MAD)2
Skewness-1.086228
Sum3.3699301 × 108
Variance9.0087894
MonotonicityNot monotonic
2024-02-04T09:23:45.801311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2023 51351
30.8%
2022 27592
16.5%
2021 18774
 
11.3%
2018 14151
 
8.5%
2020 11425
 
6.8%
2019 10860
 
6.5%
2017 8523
 
5.1%
2016 5518
 
3.3%
2015 4833
 
2.9%
2013 4455
 
2.7%
Other values (12) 9318
 
5.6%
ValueCountFrequency (%)
1997 1
 
< 0.1%
1998 1
 
< 0.1%
1999 3
 
< 0.1%
2000 7
 
< 0.1%
2002 2
 
< 0.1%
2003 1
 
< 0.1%
2008 20
 
< 0.1%
2010 23
 
< 0.1%
2011 782
0.5%
2012 1630
1.0%
ValueCountFrequency (%)
2024 3309
 
2.0%
2023 51351
30.8%
2022 27592
16.5%
2021 18774
 
11.3%
2020 11425
 
6.8%
2019 10860
 
6.5%
2018 14151
 
8.5%
2017 8523
 
5.1%
2016 5518
 
3.3%
2015 4833
 
2.9%

Make
Categorical

HIGH CORRELATION 

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
TESLA
74834 
NISSAN
13848 
CHEVROLET
13072 
FORD
8577 
BMW
 
7196
Other values (34)
49273 

Length

Max length20
Median length14
Mean length5.557446
Min length3

Characters and Unicode

Total characters926982
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFIAT
2nd rowTESLA
3rd rowBMW
4th rowRIVIAN
5th rowTESLA

Common Values

ValueCountFrequency (%)
TESLA 74834
44.9%
NISSAN 13848
 
8.3%
CHEVROLET 13072
 
7.8%
FORD 8577
 
5.1%
BMW 7196
 
4.3%
KIA 6995
 
4.2%
TOYOTA 5812
 
3.5%
VOLKSWAGEN 4717
 
2.8%
JEEP 4100
 
2.5%
HYUNDAI 4057
 
2.4%
Other values (29) 23592
 
14.1%

Length

2024-02-04T09:23:46.090022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tesla 74834
44.8%
nissan 13848
 
8.3%
chevrolet 13072
 
7.8%
ford 8577
 
5.1%
bmw 7196
 
4.3%
kia 6995
 
4.2%
toyota 5812
 
3.5%
volkswagen 4717
 
2.8%
jeep 4100
 
2.5%
hyundai 4057
 
2.4%
Other values (34) 23688
 
14.2%

Most occurring characters

ValueCountFrequency (%)
E 124884
13.5%
A 122649
13.2%
S 117030
12.6%
T 102411
11.0%
L 101940
11.0%
O 49081
 
5.3%
N 43779
 
4.7%
I 41734
 
4.5%
R 35712
 
3.9%
V 29320
 
3.2%
Other values (18) 158442
17.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 925539
99.8%
Dash Punctuation 1342
 
0.1%
Space Separator 96
 
< 0.1%
Other Punctuation 5
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 124884
13.5%
A 122649
13.3%
S 117030
12.6%
T 102411
11.1%
L 101940
11.0%
O 49081
 
5.3%
N 43779
 
4.7%
I 41734
 
4.5%
R 35712
 
3.9%
V 29320
 
3.2%
Other values (15) 156999
17.0%
Dash Punctuation
ValueCountFrequency (%)
- 1342
100.0%
Space Separator
ValueCountFrequency (%)
96
100.0%
Other Punctuation
ValueCountFrequency (%)
! 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 925539
99.8%
Common 1443
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 124884
13.5%
A 122649
13.3%
S 117030
12.6%
T 102411
11.1%
L 101940
11.0%
O 49081
 
5.3%
N 43779
 
4.7%
I 41734
 
4.5%
R 35712
 
3.9%
V 29320
 
3.2%
Other values (15) 156999
17.0%
Common
ValueCountFrequency (%)
- 1342
93.0%
96
 
6.7%
! 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 926982
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 124884
13.5%
A 122649
13.2%
S 117030
12.6%
T 102411
11.0%
L 101940
11.0%
O 49081
 
5.3%
N 43779
 
4.7%
I 41734
 
4.5%
R 35712
 
3.9%
V 29320
 
3.2%
Other values (18) 158442
17.1%

Model
Text

Distinct138
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2024-02-04T09:23:46.581564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length24
Median length7
Mean length6.3818825
Min length2

Characters and Unicode

Total characters1064498
Distinct characters38
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row500
2nd rowMODEL X
3rd row330E
4th rowR1S
5th rowMODEL 3
ValueCountFrequency (%)
model 74788
28.2%
y 32822
12.4%
3 28926
 
10.9%
leaf 13274
 
5.0%
bolt 7983
 
3.0%
s 7611
 
2.9%
ev 6648
 
2.5%
x 5429
 
2.0%
volt 4825
 
1.8%
prime 4505
 
1.7%
Other values (135) 78207
29.5%
2024-02-04T09:23:47.369965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 125149
11.8%
L 111748
 
10.5%
O 106152
 
10.0%
98218
 
9.2%
M 87547
 
8.2%
D 81731
 
7.7%
A 45626
 
4.3%
I 36297
 
3.4%
Y 35554
 
3.3%
R 35303
 
3.3%
Other values (28) 301173
28.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 881972
82.9%
Space Separator 98218
 
9.2%
Decimal Number 69361
 
6.5%
Dash Punctuation 11300
 
1.1%
Other Punctuation 3647
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 125149
14.2%
L 111748
12.7%
O 106152
12.0%
M 87547
9.9%
D 81731
9.3%
A 45626
 
5.2%
I 36297
 
4.1%
Y 35554
 
4.0%
R 35303
 
4.0%
S 27137
 
3.1%
Other values (15) 189728
21.5%
Decimal Number
ValueCountFrequency (%)
3 32990
47.6%
4 8794
 
12.7%
0 8390
 
12.1%
5 8009
 
11.5%
1 4467
 
6.4%
6 3647
 
5.3%
9 1789
 
2.6%
2 853
 
1.2%
8 336
 
0.5%
7 86
 
0.1%
Space Separator
ValueCountFrequency (%)
98218
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11300
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3647
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 881972
82.9%
Common 182526
 
17.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 125149
14.2%
L 111748
12.7%
O 106152
12.0%
M 87547
9.9%
D 81731
9.3%
A 45626
 
5.2%
I 36297
 
4.1%
Y 35554
 
4.0%
R 35303
 
4.0%
S 27137
 
3.1%
Other values (15) 189728
21.5%
Common
ValueCountFrequency (%)
98218
53.8%
3 32990
 
18.1%
- 11300
 
6.2%
4 8794
 
4.8%
0 8390
 
4.6%
5 8009
 
4.4%
1 4467
 
2.4%
6 3647
 
2.0%
. 3647
 
2.0%
9 1789
 
1.0%
Other values (3) 1275
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1064498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 125149
11.8%
L 111748
 
10.5%
O 106152
 
10.0%
98218
 
9.2%
M 87547
 
8.2%
D 81731
 
7.7%
A 45626
 
4.3%
I 36297
 
3.4%
Y 35554
 
3.3%
R 35303
 
3.3%
Other values (28) 301173
28.3%

Electric Vehicle Type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Battery Electric Vehicle (BEV)
130293 
Plug-in Hybrid Electric Vehicle (PHEV)
36507 

Length

Max length38
Median length30
Mean length31.750935
Min length30

Characters and Unicode

Total characters5296056
Distinct characters23
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBattery Electric Vehicle (BEV)
2nd rowBattery Electric Vehicle (BEV)
3rd rowPlug-in Hybrid Electric Vehicle (PHEV)
4th rowBattery Electric Vehicle (BEV)
5th rowBattery Electric Vehicle (BEV)

Common Values

ValueCountFrequency (%)
Battery Electric Vehicle (BEV) 130293
78.1%
Plug-in Hybrid Electric Vehicle (PHEV) 36507
 
21.9%

Length

2024-02-04T09:23:47.691949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-04T09:23:48.022916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
electric 166800
23.7%
vehicle 166800
23.7%
battery 130293
18.5%
bev 130293
18.5%
plug-in 36507
 
5.2%
hybrid 36507
 
5.2%
phev 36507
 
5.2%

Most occurring characters

ValueCountFrequency (%)
e 630693
11.9%
536907
10.1%
c 500400
9.4%
t 427386
 
8.1%
i 406614
 
7.7%
l 370107
 
7.0%
V 333600
 
6.3%
r 333600
 
6.3%
E 333600
 
6.3%
B 260586
 
4.9%
Other values (13) 1162563
22.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3315228
62.6%
Uppercase Letter 1073814
 
20.3%
Space Separator 536907
 
10.1%
Open Punctuation 166800
 
3.1%
Close Punctuation 166800
 
3.1%
Dash Punctuation 36507
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 630693
19.0%
c 500400
15.1%
t 427386
12.9%
i 406614
12.3%
l 370107
11.2%
r 333600
10.1%
y 166800
 
5.0%
h 166800
 
5.0%
a 130293
 
3.9%
u 36507
 
1.1%
Other values (4) 146028
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
V 333600
31.1%
E 333600
31.1%
B 260586
24.3%
P 73014
 
6.8%
H 73014
 
6.8%
Space Separator
ValueCountFrequency (%)
536907
100.0%
Open Punctuation
ValueCountFrequency (%)
( 166800
100.0%
Close Punctuation
ValueCountFrequency (%)
) 166800
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36507
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4389042
82.9%
Common 907014
 
17.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 630693
14.4%
c 500400
11.4%
t 427386
9.7%
i 406614
9.3%
l 370107
8.4%
V 333600
7.6%
r 333600
7.6%
E 333600
7.6%
B 260586
5.9%
y 166800
 
3.8%
Other values (9) 625656
14.3%
Common
ValueCountFrequency (%)
536907
59.2%
( 166800
 
18.4%
) 166800
 
18.4%
- 36507
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5296056
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 630693
11.9%
536907
10.1%
c 500400
9.4%
t 427386
 
8.1%
i 406614
 
7.7%
l 370107
 
7.0%
V 333600
 
6.3%
r 333600
 
6.3%
E 333600
 
6.3%
B 260586
 
4.9%
Other values (13) 1162563
22.0%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Eligibility unknown as battery range has not been researched
83517 
Clean Alternative Fuel Vehicle Eligible
64299 
Not eligible due to low battery range
18984 

Length

Max length60
Median length60
Mean length49.287104
Min length37

Characters and Unicode

Total characters8221089
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClean Alternative Fuel Vehicle Eligible
2nd rowClean Alternative Fuel Vehicle Eligible
3rd rowNot eligible due to low battery range
4th rowEligibility unknown as battery range has not been researched
5th rowClean Alternative Fuel Vehicle Eligible

Common Values

ValueCountFrequency (%)
Eligibility unknown as battery range has not been researched 83517
50.1%
Clean Alternative Fuel Vehicle Eligible 64299
38.5%
Not eligible due to low battery range 18984
 
11.4%

Length

2024-02-04T09:23:48.242191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-04T09:23:48.520317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
not 102501
 
8.5%
battery 102501
 
8.5%
range 102501
 
8.5%
eligibility 83517
 
6.9%
unknown 83517
 
6.9%
been 83517
 
6.9%
researched 83517
 
6.9%
has 83517
 
6.9%
as 83517
 
6.9%
eligible 83283
 
6.9%
Other values (7) 314148
26.0%

Most occurring characters

ValueCountFrequency (%)
e 1129632
13.7%
1039236
12.6%
n 648684
 
7.9%
i 629232
 
7.7%
l 609780
 
7.4%
a 584151
 
7.1%
t 538602
 
6.6%
r 436335
 
5.3%
b 352818
 
4.3%
g 269301
 
3.3%
Other values (16) 1983318
24.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6757857
82.2%
Space Separator 1039236
 
12.6%
Uppercase Letter 423996
 
5.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1129632
16.7%
n 648684
9.6%
i 629232
9.3%
l 609780
9.0%
a 584151
8.6%
t 538602
8.0%
r 436335
 
6.5%
b 352818
 
5.2%
g 269301
 
4.0%
s 250551
 
3.7%
Other values (9) 1308771
19.4%
Uppercase Letter
ValueCountFrequency (%)
E 147816
34.9%
C 64299
15.2%
A 64299
15.2%
F 64299
15.2%
V 64299
15.2%
N 18984
 
4.5%
Space Separator
ValueCountFrequency (%)
1039236
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7181853
87.4%
Common 1039236
 
12.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1129632
15.7%
n 648684
 
9.0%
i 629232
 
8.8%
l 609780
 
8.5%
a 584151
 
8.1%
t 538602
 
7.5%
r 436335
 
6.1%
b 352818
 
4.9%
g 269301
 
3.7%
s 250551
 
3.5%
Other values (15) 1732767
24.1%
Common
ValueCountFrequency (%)
1039236
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8221089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1129632
13.7%
1039236
12.6%
n 648684
 
7.9%
i 629232
 
7.7%
l 609780
 
7.4%
a 584151
 
7.1%
t 538602
 
6.6%
r 436335
 
5.3%
b 352818
 
4.3%
g 269301
 
3.3%
Other values (16) 1983318
24.1%

Electric Range
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct102
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.508993
Minimum0
Maximum337
Zeros83517
Zeros (%)50.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-04T09:23:48.803467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q384
95-th percentile259
Maximum337
Range337
Interquartile range (IQR)84

Descriptive statistics

Standard deviation93.271747
Coefficient of variation (CV)1.516392
Kurtosis0.38709605
Mean61.508993
Median Absolute Deviation (MAD)0
Skewness1.3743532
Sum10259700
Variance8699.6188
MonotonicityNot monotonic
2024-02-04T09:23:49.099894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 83517
50.1%
215 6272
 
3.8%
220 4103
 
2.5%
25 3918
 
2.3%
84 3918
 
2.3%
238 3790
 
2.3%
21 3298
 
2.0%
32 3182
 
1.9%
208 2472
 
1.5%
53 2466
 
1.5%
Other values (92) 49864
29.9%
ValueCountFrequency (%)
0 83517
50.1%
6 935
 
0.6%
8 35
 
< 0.1%
9 21
 
< 0.1%
10 162
 
0.1%
11 3
 
< 0.1%
12 164
 
0.1%
13 358
 
0.2%
14 1109
 
0.7%
15 89
 
0.1%
ValueCountFrequency (%)
337 74
 
< 0.1%
330 318
 
0.2%
322 1671
1.0%
308 485
 
0.3%
293 443
 
0.3%
291 2335
1.4%
289 646
 
0.4%
270 275
 
0.2%
266 1400
0.8%
265 124
 
0.1%

Base MSRP
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1152.7232
Minimum0
Maximum845000
Zeros163437
Zeros (%)98.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-04T09:23:49.401867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum845000
Range845000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8661.0811
Coefficient of variation (CV)7.5135829
Kurtosis602.62985
Mean1152.7232
Median Absolute Deviation (MAD)0
Skewness12.846622
Sum1.9227422 × 108
Variance75014326
MonotonicityNot monotonic
2024-02-04T09:23:49.660044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 163437
98.0%
69900 1390
 
0.8%
31950 381
 
0.2%
52900 220
 
0.1%
32250 141
 
0.1%
54950 130
 
0.1%
59900 124
 
0.1%
39995 111
 
0.1%
36900 100
 
0.1%
44100 95
 
0.1%
Other values (21) 671
 
0.4%
ValueCountFrequency (%)
0 163437
98.0%
31950 381
 
0.2%
32250 141
 
0.1%
32995 3
 
< 0.1%
33950 72
 
< 0.1%
34995 67
 
< 0.1%
36800 54
 
< 0.1%
36900 100
 
0.1%
39995 111
 
0.1%
43700 11
 
< 0.1%
ValueCountFrequency (%)
845000 1
 
< 0.1%
184400 10
< 0.1%
110950 20
< 0.1%
109000 6
 
< 0.1%
102000 15
< 0.1%
98950 20
< 0.1%
91250 5
 
< 0.1%
90700 20
< 0.1%
89100 7
 
< 0.1%
81100 22
< 0.1%

Legislative District
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)< 0.1%
Missing360
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean29.178941
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-04T09:23:49.936152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q118
median33
Q342
95-th percentile48
Maximum49
Range48
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.853534
Coefficient of variation (CV)0.50904978
Kurtosis-1.0877132
Mean29.178941
Median Absolute Deviation (MAD)11
Skewness-0.46529352
Sum4856543
Variance220.62746
MonotonicityNot monotonic
2024-02-04T09:23:50.241699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
41 10837
 
6.5%
45 10062
 
6.0%
48 9158
 
5.5%
1 7231
 
4.3%
5 7062
 
4.2%
36 6977
 
4.2%
11 6607
 
4.0%
46 6464
 
3.9%
43 6172
 
3.7%
37 4933
 
3.0%
Other values (39) 90937
54.5%
ValueCountFrequency (%)
1 7231
4.3%
2 1885
 
1.1%
3 824
 
0.5%
4 1385
 
0.8%
5 7062
4.2%
6 1593
 
1.0%
7 787
 
0.5%
8 1726
 
1.0%
9 930
 
0.6%
10 2879
 
1.7%
ValueCountFrequency (%)
49 2250
 
1.3%
48 9158
5.5%
47 3032
 
1.8%
46 6464
3.9%
45 10062
6.0%
44 4337
2.6%
43 6172
3.7%
42 2315
 
1.4%
41 10837
6.5%
40 3637
 
2.2%

DOL Vehicle ID
Real number (ℝ)

UNIQUE 

Distinct166800
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1724199 × 108
Minimum4385
Maximum4.7925477 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-04T09:23:50.620230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4385
5-th percentile1.0927191 × 108
Q11.7907406 × 108
median2.2440453 × 108
Q32.5134213 × 108
95-th percentile3.4421029 × 108
Maximum4.7925477 × 108
Range4.7925039 × 108
Interquartile range (IQR)72268068

Descriptive statistics

Standard deviation77274578
Coefficient of variation (CV)0.35570737
Kurtosis3.4968468
Mean2.1724199 × 108
Median Absolute Deviation (MAD)30844772
Skewness0.7260005
Sum3.6235965 × 1013
Variance5.9713604 × 1015
MonotonicityNot monotonic
2024-02-04T09:23:51.044542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1593721 1
 
< 0.1%
166073470 1
 
< 0.1%
148905543 1
 
< 0.1%
249787583 1
 
< 0.1%
220480571 1
 
< 0.1%
249691865 1
 
< 0.1%
131525467 1
 
< 0.1%
233740279 1
 
< 0.1%
144426796 1
 
< 0.1%
118183838 1
 
< 0.1%
Other values (166790) 166790
> 99.9%
ValueCountFrequency (%)
4385 1
< 0.1%
4777 1
< 0.1%
10286 1
< 0.1%
10734 1
< 0.1%
12050 1
< 0.1%
23145 1
< 0.1%
24629 1
< 0.1%
27702 1
< 0.1%
35325 1
< 0.1%
46112 1
< 0.1%
ValueCountFrequency (%)
479254772 1
< 0.1%
479114996 1
< 0.1%
478935460 1
< 0.1%
478934571 1
< 0.1%
478926346 1
< 0.1%
478925947 1
< 0.1%
478925163 1
< 0.1%
478924358 1
< 0.1%
478916028 1
< 0.1%
478910428 1
< 0.1%
Distinct835
Distinct (%)0.5%
Missing10
Missing (%)< 0.1%
Memory size1.3 MiB
2024-02-04T09:23:51.705914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length33
Median length32
Mean length28.439799
Min length24

Characters and Unicode

Total characters4743474
Distinct characters20
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique286 ?
Unique (%)0.2%

Sample

1st rowPOINT (-120.524012 46.5973939)
2nd rowPOINT (-122.817545 46.98876)
3rd rowPOINT (-122.1298876 47.4451257)
4th rowPOINT (-122.1873 47.820245)
5th rowPOINT (-122.2012521 47.3931814)
ValueCountFrequency (%)
point 166790
33.3%
47.67668 4252
 
0.8%
122.12302 4252
 
0.8%
122.1873 3115
 
0.6%
47.820245 3115
 
0.6%
122.20264 2840
 
0.6%
47.6785 2840
 
0.6%
122.16937 2663
 
0.5%
47.571015 2663
 
0.5%
122.201905 2652
 
0.5%
Other values (1660) 305188
61.0%
2024-02-04T09:23:52.832796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 492336
 
10.4%
1 377714
 
8.0%
4 335804
 
7.1%
. 333580
 
7.0%
333580
 
7.0%
7 314789
 
6.6%
5 301633
 
6.4%
3 203552
 
4.3%
6 202005
 
4.3%
8 197371
 
4.2%
Other values (10) 1651110
34.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2741994
57.8%
Uppercase Letter 833950
 
17.6%
Other Punctuation 333580
 
7.0%
Space Separator 333580
 
7.0%
Dash Punctuation 166790
 
3.5%
Open Punctuation 166790
 
3.5%
Close Punctuation 166790
 
3.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 492336
18.0%
1 377714
13.8%
4 335804
12.2%
7 314789
11.5%
5 301633
11.0%
3 203552
7.4%
6 202005
7.4%
8 197371
7.2%
9 172064
 
6.3%
0 144726
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
O 166790
20.0%
T 166790
20.0%
N 166790
20.0%
I 166790
20.0%
P 166790
20.0%
Other Punctuation
ValueCountFrequency (%)
. 333580
100.0%
Space Separator
ValueCountFrequency (%)
333580
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 166790
100.0%
Open Punctuation
ValueCountFrequency (%)
( 166790
100.0%
Close Punctuation
ValueCountFrequency (%)
) 166790
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3909524
82.4%
Latin 833950
 
17.6%

Most frequent character per script

Common
ValueCountFrequency (%)
2 492336
12.6%
1 377714
9.7%
4 335804
8.6%
. 333580
8.5%
333580
8.5%
7 314789
 
8.1%
5 301633
 
7.7%
3 203552
 
5.2%
6 202005
 
5.2%
8 197371
 
5.0%
Other values (5) 817160
20.9%
Latin
ValueCountFrequency (%)
O 166790
20.0%
T 166790
20.0%
N 166790
20.0%
I 166790
20.0%
P 166790
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4743474
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 492336
 
10.4%
1 377714
 
8.0%
4 335804
 
7.1%
. 333580
 
7.0%
333580
 
7.0%
7 314789
 
6.6%
5 301633
 
6.4%
3 203552
 
4.3%
6 202005
 
4.3%
8 197371
 
4.2%
Other values (10) 1651110
34.8%
Distinct76
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Memory size1.3 MiB
2024-02-04T09:23:53.376050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length112
Median length110
Mean length44.349777
Min length10

Characters and Unicode

Total characters7397321
Distinct characters36
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowPACIFICORP
2nd rowPUGET SOUND ENERGY INC
3rd rowPUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
4th rowPUGET SOUND ENERGY INC
5th rowPUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
ValueCountFrequency (%)
of 156743
12.6%
146822
11.8%
wa 102355
 
8.2%
tacoma 101048
 
8.1%
sound 99739
 
8.0%
energy 99739
 
8.0%
puget 98811
 
7.9%
inc||city 61337
 
4.9%
power 36727
 
2.9%
inc 33917
 
2.7%
Other values (114) 310920
24.9%
2024-02-04T09:23:54.018285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1081363
14.6%
O 543795
 
7.4%
N 527092
 
7.1%
T 513721
 
6.9%
A 499156
 
6.7%
E 483730
 
6.5%
I 404993
 
5.5%
C 402838
 
5.4%
Y 270111
 
3.7%
U 259031
 
3.5%
Other values (26) 2411491
32.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5604845
75.8%
Space Separator 1081363
 
14.6%
Math Symbol 254096
 
3.4%
Close Punctuation 142278
 
1.9%
Dash Punctuation 142278
 
1.9%
Open Punctuation 142278
 
1.9%
Decimal Number 24893
 
0.3%
Other Punctuation 5290
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 543795
 
9.7%
N 527092
 
9.4%
T 513721
 
9.2%
A 499156
 
8.9%
E 483730
 
8.6%
I 404993
 
7.2%
C 402838
 
7.2%
Y 270111
 
4.8%
U 259031
 
4.6%
G 215415
 
3.8%
Other values (14) 1484963
26.5%
Decimal Number
ValueCountFrequency (%)
1 23111
92.8%
2 729
 
2.9%
3 674
 
2.7%
5 379
 
1.5%
Other Punctuation
ValueCountFrequency (%)
& 4544
85.9%
# 379
 
7.2%
, 367
 
6.9%
Space Separator
ValueCountFrequency (%)
1081363
100.0%
Math Symbol
ValueCountFrequency (%)
| 254096
100.0%
Close Punctuation
ValueCountFrequency (%)
) 142278
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 142278
100.0%
Open Punctuation
ValueCountFrequency (%)
( 142278
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5604845
75.8%
Common 1792476
 
24.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 543795
 
9.7%
N 527092
 
9.4%
T 513721
 
9.2%
A 499156
 
8.9%
E 483730
 
8.6%
I 404993
 
7.2%
C 402838
 
7.2%
Y 270111
 
4.8%
U 259031
 
4.6%
G 215415
 
3.8%
Other values (14) 1484963
26.5%
Common
ValueCountFrequency (%)
1081363
60.3%
| 254096
 
14.2%
) 142278
 
7.9%
- 142278
 
7.9%
( 142278
 
7.9%
1 23111
 
1.3%
& 4544
 
0.3%
2 729
 
< 0.1%
3 674
 
< 0.1%
# 379
 
< 0.1%
Other values (2) 746
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7397321
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1081363
14.6%
O 543795
 
7.4%
N 527092
 
7.1%
T 513721
 
6.9%
A 499156
 
6.7%
E 483730
 
6.5%
I 404993
 
5.5%
C 402838
 
5.4%
Y 270111
 
3.7%
U 259031
 
3.5%
Other values (26) 2411491
32.6%

2020 Census Tract
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2088
Distinct (%)1.3%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.2977092 × 1010
Minimum1.0010201 × 109
Maximum5.6033 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-02-04T09:23:54.341843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.0010201 × 109
5-th percentile5.3011041 × 1010
Q15.303301 × 1010
median5.303303 × 1010
Q35.3053073 × 1010
95-th percentile5.3067012 × 1010
Maximum5.6033 × 1010
Range5.503198 × 1010
Interquartile range (IQR)20063300

Descriptive statistics

Standard deviation1.5697544 × 109
Coefficient of variation (CV)0.029630815
Kurtosis751.92796
Mean5.2977092 × 1010
Median Absolute Deviation (MAD)27702
Skewness-26.966776
Sum8.836314 × 1015
Variance2.4641288 × 1018
MonotonicityNot monotonic
2024-02-04T09:23:54.634222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.30330282 × 10101914
 
1.1%
5.30330285 × 10101001
 
0.6%
5.303303232 × 1010763
 
0.5%
5.30330262 × 1010713
 
0.4%
5.30330093 × 1010647
 
0.4%
5.30670112 × 1010624
 
0.4%
5.303303232 × 1010564
 
0.3%
5.303303222 × 1010545
 
0.3%
5.303302501 × 1010532
 
0.3%
5.306105211 × 1010529
 
0.3%
Other values (2078) 158963
95.3%
ValueCountFrequency (%)
1001020100 2
< 0.1%
1081041901 1
< 0.1%
1097006803 1
< 0.1%
1117030352 1
< 0.1%
2020000206 1
< 0.1%
4013115900 1
< 0.1%
4013216901 1
< 0.1%
4013610301 1
< 0.1%
4013610302 1
< 0.1%
4013610500 1
< 0.1%
ValueCountFrequency (%)
5.60330001 × 10101
 
< 0.1%
5.307794001 × 10105
 
< 0.1%
5.307794001 × 10103
 
< 0.1%
5.307794 × 10102
 
< 0.1%
5.307794 × 10107
 
< 0.1%
5.307794 × 10107
 
< 0.1%
5.307794 × 10103
 
< 0.1%
5.30770034 × 101033
< 0.1%
5.30770032 × 101040
< 0.1%
5.30770031 × 101020
< 0.1%

Interactions

2024-02-04T09:23:34.428221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:21.687581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:23.616779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:25.992361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:28.450322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:30.445883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:32.355147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:34.705422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:21.986595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:23.880669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:26.406340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:28.701734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:30.724451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:32.637627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:34.977425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:22.266353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:24.148873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:26.840194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:28.961093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:30.987671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:32.916772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:35.274267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:22.552268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:24.431449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:27.300999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:29.233450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:31.273059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:33.209191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:35.549079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:22.817731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:24.775506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:27.630760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:29.477216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:31.531578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:33.478454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:35.845200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:23.084487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:25.148765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:27.891496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:29.916561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:31.794849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:33.740521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:36.126391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:23.351589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:25.565530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:28.170050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:30.181571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:32.056066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-04T09:23:34.148525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-02-04T09:23:54.886847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2020 Census TractBase MSRPClean Alternative Fuel Vehicle (CAFV) EligibilityDOL Vehicle IDElectric RangeElectric Vehicle TypeLegislative DistrictMakeModel YearPostal CodeState
2020 Census Tract1.0000.0000.0090.011-0.0150.013-0.1870.0090.0110.0580.991
Base MSRP0.0001.0000.024-0.0350.1160.0220.0110.180-0.190-0.0030.040
Clean Alternative Fuel Vehicle (CAFV) Eligibility0.0090.0241.0000.092-0.7170.743-0.0180.5910.455-0.0050.010
DOL Vehicle ID0.011-0.0350.0921.000-0.1600.078-0.0170.1130.348-0.0060.009
Electric Range-0.0150.116-0.717-0.1601.0000.525-0.0040.378-0.6970.0550.010
Electric Vehicle Type0.0130.0220.7430.0780.5251.000-0.0680.769-0.1590.1090.015
Legislative District-0.1870.011-0.018-0.017-0.004-0.0681.0000.086-0.014-0.3381.000
Make0.0090.1800.5910.1130.3780.7690.0861.0000.092-0.0910.000
Model Year0.011-0.1900.4550.348-0.697-0.159-0.0140.0921.000-0.0610.030
Postal Code0.058-0.003-0.005-0.0060.0550.109-0.338-0.091-0.0611.0000.935
State0.9910.0400.0100.0090.0100.0151.0000.0000.0300.9351.000

Missing values

2024-02-04T09:23:36.768436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-04T09:23:37.946370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-04T09:23:39.993181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

VIN (1-10)CountyCityStatePostal CodeModel YearMakeModelElectric Vehicle TypeClean Alternative Fuel Vehicle (CAFV) EligibilityElectric RangeBase MSRPLegislative DistrictDOL Vehicle IDVehicle LocationElectric Utility2020 Census Tract
03C3CFFGE4EYakimaYakimaWA98902.02014FIAT500Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible87014.01593721POINT (-120.524012 46.5973939)PACIFICORP5.307700e+10
15YJXCBE40HThurstonOlympiaWA98513.02017TESLAMODEL XBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible20002.0257167501POINT (-122.817545 46.98876)PUGET SOUND ENERGY INC5.306701e+10
23MW39FS03PKingRentonWA98058.02023BMW330EPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range20011.0224071816POINT (-122.1298876 47.4451257)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+10
37PDSGABA8PSnohomishBothellWA98012.02023RIVIANR1SBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0021.0260084653POINT (-122.1873 47.820245)PUGET SOUND ENERGY INC5.306105e+10
45YJ3E1EB8LKingKentWA98031.02020TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible322033.0253771913POINT (-122.2012521 47.3931814)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+10
55UX43EU02RKitsapPoulsboWA98370.02024BMWX5Plug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible39023.0259427829POINT (-122.64177 47.737525)PUGET SOUND ENERGY INC5.303594e+10
62C4RC1H7XJKitsapPort OrchardWA98367.02018CHRYSLERPACIFICAPlug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible33026.0477087012POINT (-122.6847073 47.50524)PUGET SOUND ENERGY INC5.303509e+10
71G1FX6S01HKitsapPoulsboWA98370.02017CHEVROLETBOLT EVBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible238023.0214494213POINT (-122.64177 47.737525)PUGET SOUND ENERGY INC5.303509e+10
85YJ3E1EA2JKitsapPort OrchardWA98366.02018TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible215026.0280785123POINT (-122.639265 47.5373)PUGET SOUND ENERGY INC5.303509e+10
9WBY7Z6C59JKingDuvallWA98019.02018BMWI3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible114045.0129133343POINT (-121.9810747 47.7377962)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+10
VIN (1-10)CountyCityStatePostal CodeModel YearMakeModelElectric Vehicle TypeClean Alternative Fuel Vehicle (CAFV) EligibilityElectric RangeBase MSRPLegislative DistrictDOL Vehicle IDVehicle LocationElectric Utility2020 Census Tract
166790KM8JBDD24RKingSeattleWA98102.02024HYUNDAITUCSONPlug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible33043.0258782814POINT (-122.32226 47.64058)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)5.303301e+10
1667911C4RJXN60RWhatcomBellinghamWA98225.02024JEEPWRANGLERPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range21042.0261031214POINT (-122.486115 48.761615)PUGET SOUND ENERGY INC||PUD NO 1 OF WHATCOM COUNTY5.307300e+10
1667921N4AZ0CP7DSkagitAnacortesWA98221.02013NISSANLEAFBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible75010.0259757998POINT (-122.615305 48.501275)PUGET SOUND ENERGY INC5.305794e+10
166793WP0AA2Y16NKingKirklandWA98033.02022PORSCHETAYCANBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0048.0199014224POINT (-122.20264 47.6785)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303302e+10
166794KNDC3DLC0NKittitasEastonWA98925.02022KIAEV6Battery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0013.0212130950POINT (-121.1761632 47.24106)PUGET SOUND ENERGY INC5.303798e+10
1667953FA6P0SU4DSpokaneSpokaneWA99223.02013FORDFUSIONPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range1906.0239527123POINT (-117.369705 47.62637)BONNEVILLE POWER ADMINISTRATION||AVISTA CORP||INLAND POWER & LIGHT COMPANY5.306300e+10
1667965YJYGDEE5MKingSammamishWA98074.02021TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0045.0148715479POINT (-122.0313266 47.6285782)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)5.303303e+10
1667977SAYGDEE5NSnohomishMukilteoWA98275.02022TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0021.0220504406POINT (-122.299965 47.94171)PUGET SOUND ENERGY INC5.306104e+10
1667981G1RH6E43DLewisMossyrockWA98564.02013CHEVROLETVOLTPlug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible38020.0156418475POINT (-122.487535 46.5290135)BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PUD NO 1 OF LEWIS COUNTY5.304197e+10
1667995YJSA1E27HPierceGig HarborWA98332.02017TESLAMODEL SBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible210026.0169045789POINT (-122.589645 47.342345)BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PENINSULA LIGHT COMPANY5.305307e+10